SynthesisFrontiers in oncology2025
Artificial intelligence based on ultrasound for initial diagnosis of malignant ovarian cancer: a systematic review and meta-analysis.
Synthesis in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.Journal of ovarian research · 2026Pooled it
- A cross-validated deep learning framework for automated detection of DMBA-induced ovarian cancer from histopathological images with CA-125 biomarker support.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This meta-analysis aimed to evaluate the diagnostic performance of artificial intelligence (AI) in ultrasound imaging for the initial diagnosis of malignant ovarian cancer, comparing its performance to that of sonographers. Methods: A systematic literature search was conducted in PubMed, Web of Science, Embase, and the Cochrane Library up to February 2025. Inclusion criteria targeted studies employing AI algorithms to analyze ultrasound images in patients with suspected ovarian cancer, using pathology as the reference standard. Bivariate random-effects models were utilized to aggregate sensitivity, specificity, and area under the curve (AUC). The methodological quality of the included studies was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results: Eighteen studies encompassing a total of 22,697 total patients/images/lesions were analyzed. AI demonstrated a sensitivity of 0.95 (95% CI: 0.88-0.98) and specificity of 0.95 (95% CI: 0.89-0.98) in internal validation sets, yielding an AUC of 0.98. In external validation, sensitivity was 0.78 (95% CI: 0.56-0.91) and specificity was 0.88 (95% CI: 0.76-0.95), with an AUC of 0.91. In comparison, sonographers exhibited a sensitivity of 0.83 (95% CI: 0.62-0.94), specificity of 0.84 (95% CI: 0.79-0.88), and an AUC of 0.87. These results indicate that ultrasound-based AI significantly outperforms sonographer diagnostics. Meta-regression analysis indicated that the heterogeneity was primarily attributed to the analysis method (image-based vs. patient-based, specificity Conclusions: AI based on ultrasound diagnosis demonstrates excellent performance for malignant ovarian cancer detection, with potentially superior performance compared to sonographers. Despite high heterogeneity across studies and the observed publication bias, these results indicate the potential for AI integration into clinical practice. Further studies with external, multicenter prospective head-to-head design are still needed.
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